Priani Sani Ega, Fakih Taufik Muhammad, Wilar Gofarana, Chaerunisaa Anis Yohana, Sopyan Iyan
Doctoral Program of Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang 45363, Indonesia.
Faculty of Mathematics and Natural Sciences, Bandung Islamic University, Bandung 40116, Indonesia.
Pharmaceutics. 2025 May 27;17(6):701. doi: 10.3390/pharmaceutics17060701.
: The Self-Nanoemulsifying Drug Delivery System (SNEDDS) has been widely applied in oral drug delivery, particularly for poorly water-soluble compounds. The successful development of SNEDDS largely depends on the precise composition of its components. This narrative review provides an in-depth analysis of Quality by Design (QbD), Design of Experiment (DoE), and in silico approach applications in SNEDDS development. : The review is based on publications from 2020 to 2025, sourced from reputable scientific databases (Pubmed, Science direct, Taylor and francis, and Scopus). : Quality by Design (QbD) is a systematic and scientific approach that enhances product quality while ensuring the robustness and reproducibility of SNEDDS, as outlined in the Quality Target Product Profile (QTPP). DoE was integrated into the QbD framework to systematically evaluate the effects of predefined factors, particularly Critical Material Attributes (CMAs) and Critical Process Parameters (CPP), on the desired responses (Critical Quality Attributes/CQA), ultimately leading to the identification of the optimal SNEDDS formulation. Various DoEs, including the mixture design, response surface methodology, and factorial design, have been widely applied to SNEDDS formulations. The experimental design facilitates the analysis of the relationship between CQA and CMA/CPP, enabling the identification of optimized formulations with enhanced biopharmaceutical, pharmacokinetic, and pharmacodynamic profiles. As an essential addition to this review, in silico approach emerges as a valuable tool in the development of SNEDDS, offering deep insights into self-assembly dynamics, molecular interactions, and emulsification behaviour. By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. : The integration of QbD, DoE, and in silico approaches holds significant potential in the development of SNEDDS. These strategies enable a more efficient, rational, and predictive formulation process.
自纳米乳化药物递送系统(SNEDDS)已广泛应用于口服药物递送,特别是对于水溶性差的化合物。SNEDDS的成功开发很大程度上取决于其成分的精确组成。本叙述性综述深入分析了质量源于设计(QbD)、实验设计(DoE)和计算机模拟方法在SNEDDS开发中的应用。本综述基于2020年至2025年的出版物,这些出版物来自著名的科学数据库(PubMed、ScienceDirect、Taylor and Francis以及Scopus)。质量源于设计(QbD)是一种系统的科学方法,可提高产品质量,同时确保SNEDDS的稳健性和可重复性,如质量目标产品概况(QTPP)中所述。DoE被整合到QbD框架中,以系统地评估预定义因素,特别是关键物料属性(CMA)和关键工艺参数(CPP)对预期响应(关键质量属性/CQA)的影响,最终确定最佳的SNEDDS配方。各种DoE,包括混合设计、响应面方法和析因设计,已广泛应用于SNEDDS配方。实验设计有助于分析CQA与CMA/CPP之间的关系,从而能够确定具有增强生物药剂学、药代动力学和药效学特征的优化配方。作为本综述的重要补充,计算机模拟方法成为SNEDDS开发中的一种有价值的工具,可深入了解自组装动力学、分子相互作用和乳化行为。通过将分子模拟与机器学习相结合,这种方法能够进行合理且高效的优化。QbD、DoE和计算机模拟方法的整合在SNEDDS的开发中具有巨大潜力。这些策略能够实现更高效、合理和可预测的配方过程。
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